\section{Algorithms}
	In this section we will present our aproach to tackle the speaker recognition problem.

    An utterance of a user is collected during enrollment procedure.
    Further processing of the utterance follows following steps:
    \subsection{VAD}
        Signals must be first filtered to rule out the silence part, otherwise the
        training might be seriously biased. Therefore \textbf{Voice Activity Detection} must
        be first performed.

        An observation found is that, the corpus provided is nearly noise-free.
        Therefore we use a simple energy-based approach
        to remove the silence part, by simply remove the frames that the average
        energy is below 0.01 times the average energy of the whole utterance.

        This energy-based method is found to work well on database, but not
        on GUI.
        We use LTSD(Long-Term Spectral Divergence) \cite{ltsd1}
        algorithm on GUI, as well as noise reduction technique from SOX\cite{sox} to gain better result in real-life application.

        LTSD algorithm splits a utterance into overlapped frames, and give scores for each frame on
        the probability that there is voice activity in this frame. This probability will be accumulated
        to extract all the intervals with voice activity. A picture depicting the principle of LTSD is as followed:

        \begin{figure}[H]
          \centering
          \includegraphics[width=0.6\textwidth]{img/ltsd.png}
        \end{figure}
		Since this is not our primary-task, we shall not expand details here. For further
		information on how these works, please consult original paper.


        \input{feature}
        \input{model}

